Gas emission monitoring and detection

ABSTRACT

Systems, methods, and a computer readable medium are provided for monitoring and detecting a gas emission. Sensor data including gas concentration and wind data associated with a gas emission from an emission source is received from Near-Field and Far-Field sensors configured within a gas production and distribution environment. The sensor data can be provided as inputs to a Near-Field dispersion model to determine an emission rate associated with the gas emission and one or more source locations associated with the gas emission. The emission rate can be included in emission data and provided for output. Related apparatus, systems, techniques, and articles are also described.

RELATED APPLICATION

This application claims priority under 35 U.S.C. § 119(e) to U.S.Provisional Application No. 62/874,755 filed Jul. 16, 2019, the entirecontents of which are hereby expressly incorporated by reference herein.

BACKGROUND

Monitoring and detection of gas leaks is commonly performed byinspection of industrial assets, such as assets configured in gasproduction and distribution environment. Inspections can be performed toensure operational safety of the assets and to determine the presence ofleaks or gas emissions which can be emanating from an emission source.Gas leaks in these environments can create hazardous operatingconditions for personnel assigned to operate, maintain, and repair theindustrial assets and can reduce production rates. Gas leaks can occuras a result of equipment failures which can cause the release ofunplanned, or fugitive gaseous emission. Gas leaks can also occur as aresult of venting that is part of the normal and expected operation ofthe equipment or assets. Localized weather patterns can alter theconcentration, location, and distribution of the gas emission making itdifficult to accurately determine an emission source associated with thegas leak.

SUMMARY

In one aspect, methods are provided. In one embodiment, the method caninclude receiving Near-Field sensor data and Far-Field sensor data fromone or more sensors configured with respect to a gas production anddistribution environment. The sensor data can be associated with a gasbeing emitted from an emission source. The method can also includefiltering the received sensor data. The method can further includedetermining gas concentration data associated with the gas emission. Themethod can include determining an emission rate corresponding to the gasemission. The method can also include generating emission datacorresponding to the gas emission. The emission data can include thedetermined emission rate and one or more source locations associatedwith the gas emission. The method can further include providing theemission data.

Non-transitory computer program products (i.e., physically embodiedcomputer program products) are also described that store instructions,which when executed by one or more data processors of one or morecomputing systems, causes at least one data processor to performoperations herein. Similarly, computer systems are also described thatmay include one or more data processors and memory coupled to the one ormore data processors. The memory may temporarily or permanently storeinstructions that cause at least one processor to perform one or more ofthe operations described herein. In addition, methods can be implementedby one or more data processors either within a single computing systemor distributed among two or more computing systems. Such computingsystems can be connected and can exchange data and/or commands or otherinstructions or the like via one or more connections, including aconnection over a network (e.g. the Internet, a wireless wide areanetwork, a local area network, a wide area network, a wired network, orthe like), via a direct connection between one or more of the multiplecomputing systems, etc.

DESCRIPTION OF DRAWINGS

These and other features will be more readily understood from thefollowing detailed description taken in conjunction with theaccompanying drawings, in which:

FIG. 1A is a block diagram illustrating one example of a system formonitoring and detecting gas emissions;

FIG. 1B is a diagram illustrating one example of a client grid ofNear-Field and Far-Field sensors for use in a system for monitoring anddetecting gas emissions;

FIGS. 1C-1E are diagrams illustrating an exemplary process for selectingclient grid dimensions using the system for monitoring and detecting gasemissions;

FIGS. 2A-2B are block diagrams illustrating a client device for use in asystem for monitoring and detecting gas emissions;

FIGS. 3A-3C are block diagrams illustrating one exemplary embodiment ofa mobile platform including for use in in a system for monitoring anddetecting gas emissions;

FIG. 4 is a block diagram illustrating one exemplary embodiment of anarchitecture of a system for monitoring and detecting gas emissions;

FIG. 5 is a flowchart illustrating one exemplary embodiment of a methodfor monitoring and detecting gas emissions using the system of FIG. 4 ;and

FIG. 6 is a block diagram of an exemplary computing device in accordancewith an illustrative implementation of the gas monitoring and detectionsystem of FIGS. 1 and 4 .

It is noted that the drawings are not necessarily to scale. The drawingsare intended to depict only typical aspects of the subject matterdisclosed herein, and therefore should not be considered as limiting thescope of the disclosure.

DETAILED DESCRIPTION

Gas production and distribution environments include networks ofpipelines coupling industrial assets, such as motors, compressors,separators, and accumulation tanks, used to store, process, anddistribute gas throughout the production and distribution environment.The various industrial assets can be an emission source of a gas thatmay be released into the atmosphere. Operators of these environmentsperform monitoring and inspection of the pipelines and industrial assetsto detect leaks or emissions which may be released during failure of anindustrial asset and may cause unsafe operating conditions or reduceoperating production rates. Operators also perform monitoring andinspection of the pipelines and industrial assets to ensure venting ofgases are occurring in accordance with the expected and normaloperational characteristics.

Methane leak detection is one of the pressing needs in the oil and gasindustry today. Determining the location, type of emission source, andrate of emission can be a time-consuming, error prone process. Theprocess can be further complicated by the presence of prevailingseasonal wind or weather conditions which may distribute the gasemission in a manner which can make determining emission locations,sources, and rates challenging.

Current emission assessment methods can include emission monitoringwhich may be required by law for the day-to-day operation of a gasproduction and distribution environment. Emission monitoring in acontinuous manner can yield emission estimates with uncertainties in therange of +/−5% provided sensors are installed in close proximity to all(known) leak sources. In practice, it is cost prohibitive to installdedicated sensors at every component and every equipment that has apotential to emit depending on equipment failure or venting as a part ofnormal operation. In addition, there is a lack of commercially availableemission monitoring systems employing leak analysis methods toaccurately determine individual leak rates based on type of equipment(vessels, compressors, pipe seals and flanges, valves, actuators,manifolds, etc.) and pinpointing the leak sources and their locationsunder prevailing wind conditions. Current emission assessment methodscan also include emission source simulation. Computer models areavailable for estimating emissions from some types of emission sources.These models apply empirical correlations and/or fundamental engineeringprinciples to develop rigorous emission estimates based on the specificoperating and design parameters of the emission source. Simulators offerthe ability to accurately predict emissions from sources within +/−25%uncertainty, however they require more time, effort, user knowledge, andinput data to generate emission rate estimates. In addition, thesimulation models require inputs of the specific emission source designand operating input data which can be difficult to obtain. Anothercurrent method of estimating emission rates can include statisticalapproached in which emission factors are determined for the averageemission from a group of sources is related to an appropriate activity.However, the use of emission factors is often an oversimplification andsubject to even higher uncertainties compared to the prior two methodsmentioned above. Although the method becomes a statistically validapproach when considering aggregate emission from a large number ofsources, it is less useful when applied for determining an emission ratefor a single emission source.

An improved gas emission monitoring and detection system can beconfigured to receive sensor data from Near-Field and Far-Field sensorswhich are strategically deployed at predetermined locations throughout agas production and distribution environment and to determine an emissionrate associated with an emission leak emanating from an emission sourceor periodic vent gas emissions associated with specific equipment. Thesensors can be deployed in a grid-like arrangement and can be configuredto communicate data with each other. Further, the grid spatialdimensions (X and Y) are predetermined based on minimum thresholddetection level of sensors under the prevailing wind velocity rangeobserved for the region and diffusion/advection of gas plume relative tooutermost sensors. The received sensor data can include wind dataassociated with the prevailing or current weather conditions as well asgas concentration data corresponding to an emission source in proximityof the Near-Field or Far-Field sensor. The collection of sensor data canbe configured with respect to weather forecasting data received from apublic or private weather forecasting source so as to coordinate sensordata collection during weather conditions which are least likely tointroduce noise in the received sensor data signals. The emission ratecan be determined using a plume advection-diffusion model which canreceive the sensed gas concentration data and the wind data as inputs todetermine an emission rate associated with a particular emission source.The emission rate can be included in emission data generated by theimproved gas monitoring and detection system described herein.

The improved gas monitoring and detection system can thus automate theotherwise manually intensive tasks of manually monitoring and detectingemission sources for leaks and determining emission rates. Such animproved gas monitoring and detection system can provide gas productionand distribution operators with greater insight into the currentoperating conditions of the industrial assets configured in the gasproduction and distribution environment and can also aid in identifyinggas emission locations to aid maintenance and repair operations based onearly detection of leaks. The improved gas detection and monitoringsystem can generate emission data, including emission rates andlocations of emission sources, to assist operators in planningconfigurations of industrial assets and deployment of monitoring andinspection personnel or equipment, while maintaining production atacceptable and profitable levels.

Embodiments of systems and corresponding methods for generating emissiondata based on sensor data associated with a plume of gas emanating froman industrial asset configured in a gas production and distributionenvironment are discussed herein. However, embodiments of the disclosurecan be employed for generating emission data based on sensor dataassociated with other types of assets or other sources of gas which arenot associated with a gas production and distribution environmentwithout limit.

FIG. 1 is a block diagram illustrating one example of an architecture100A of a system for monitoring and detecting gas emissions. Thearchitecture 100A includes a gas production and distribution environment105 which has been divided into four quadrants, e.g., quadrants110A-110D. The gas production and distribution environment 105 alsoincludes a number of emission sources 115, such as equipment or assetswhich may be sources of a gas leak or emission 120. The architecture100A also includes a number of clients 125, which can include one ormore sensors which can be configured to detect sensor data correspondingto the localized wind conditions, e.g., W. For example, as shown in FIG.1 , the wind, W, is blowing across the gas production and distributionenvironment 105 from the left side of FIG. 1 , to the right side of FIG.1 . The clients 125 can also include one or more sensors which can beconfigured to detect sensor data associated with the gas emissions orleaks 120. The clients 125 can be configured to provide the sensor datavia a network 130 to the emission analyzer 135. The emission analyzer135 can be configured with a data filter module 140, a prediction module145, an optimization module 150, a control module 155, a maintenancemodule 160, and/or a memory 165. The emission analyzer 135 and themodules configured therein can receive the sensor data from one or moreclients 125 and can generate emission data used in determining a gasemission or leak from one or more gas sources 115.

As shown in FIG. 1 , the architecture 100A includes a gas production anddistribution environment 105. The gas production and distributionenvironment 105 can occupy a geographic location that can be configuredinto a grid of quadrants 110. In some embodiments, any of the quadrants110 can be associated with an area of arbitrary dimensions and shape.The quadrants 110 can be identifiable by an identifier, label, or othersimilar means which can be used to identify the location of any emissionsources 115, emissions 120, and/or clients 125 which may be presentwithin the quadrants 110. In some embodiments, the gas production anddistribution environment 105 can be configured in a rural, urban, orsuburban environment. For example, the gas detection and monitoringsystem described herein can be configured for use in a city wherein anumber of clients 125 can be configured with respect to existinginfrastructure elements, such as light poles, street lights, publicsafety and communication devices, as well as buildings or otherstructures which may be present within the city. In these examples, theclients 125 can be distributed throughout the city and configured todetect gas emissions which may be

As further shown in FIG. 1 , a number of emission sources 115 can bepresent within the gas production and distribution environment 105. Insome embodiments, one or more of the emission sources 115 can be locatedoutside of the gas production and distribution environment. The emissionsources 115 can include equipment associated with the production anddistribution of a gas. For example, the emission sources 115 can includea compressor, a separator, a pump, a storage tank, a valve, an actuator,and/or one or more components of an emission source 115. Additionalexamples of emission sources 115 can include a piping components such asflanges, pipe seals such as gaskets, O-rings, dynamic seals for integralengine/compressor, such as cross piece, actuator vents, flares, burnersand fuel piping, boilers, heaters, gas storage vessels, dehydrationunits, sour gas treating units, cryogenic gas treatment units, heatexchangers, etc. In some embodiments, the emission source 115 can be oneor more storage tanks configured on a mobile platform such as atransport truck configured to carry liquid natural gas (LNG), and/orcompressed natural gas (CNG).

The emission sources 115 can emit or leak gas 120 into the environmentas a result of normal, required, and expected operating conditions ofthe emission source 115, such as venting of a compressor overflow line,or as a result of unexpected, abnormal operating conditions for whichroutine venting or gas emission is not required, such as a failure of avalve configured on a separator. Gas emissions 120 occurring duringunexpected, anomalous operating conditions can be referred to asfugitive emissions and can reflect gas emissions that have escaped theoperating equipment used to contain the gas for production and/ordistribution operations. Fugitive gas emissions 120 can be caused byfailures of a seal, gasket, surface, flange or the like that isassociated with an emission source 115. In some embodiments, gasemissions 120 can occur as a result of corrosion, vibration, electricalor mechanical failures which may be present with respect to the emissionsource 115. Gas emissions 120 can include common gas species such asmethane, ethane, propane, butane, hexane, and other hydrocarbons such asnatural gas liquids (C5, C6, C8-10), mixtures of alkanes, sour gasesinclude H2S, SOx, carbon disulfide, unsaturated HCs/petrochemicals suchas ethylene, propylene, or the like which can be emitted from variousemission sources 115 and/or detected via clients 125.

As shown in FIG. 1 , a number of client 125 can be configured withrespect to the gas production and distribution environment 105. Theclients 125 can include a computing device, including a processor, oneor more sensors, and a memory storing executable instructions configuredto cause the sensors to generate sensor data associated with weatherconditions, such as a wind velocity associated with the wind (W), and/ora gas emission 120. The clients 125 can be arranged in proximity to anemission source 115 or a potential emission source 115. In someembodiments, the clients can be configured on a mobile platform, such asa CNG or LNG transport truck. In this way, the clients 125 can beconfigured to generate and transmit sensor data associated with thecurrent weather conditions and a gas emission 120 that may be emanatingfrom a gas emission source 115.

For example, as shown in FIG. 1 , in quadrant 110A, the client 125A hasbeen configured with respect to an emission source 115A which may be apotential emission source for a fugitive gas emission 120A, such as acompressor. The client 125A may be positioned with respect to thepotential emission source 115A and prevailing weather conditions, suchas the wind, W, such that it is most likely to detect the gas emission115A and generate sensor data corresponding to the gas emission 115A.Similarly, in quadrant 110C, client 125C may also be configured withrespect to emission source 115C which may also be a potential emissionsource for a fugitive gas emission 120C, for example, a gas emanatingfrom a flange or coupling of a separator which has experienced a failureand is exhibiting anomalous operating conditions. Clients 125A and 125Ccan be configured as Far-Field sensors which can be positioned 30 to 100feet away from potential emission sources 115A and 115C, respectively.In some embodiments, the Far-Field sensors can be positioned 10 to 100feet, or 50 to 200 feet from the potential emission source 115. In someembodiments, the Far-Field sensors can be positioned 4 to 8 feet abovethe surface of the ground. The Far-Field sensors can be positioned onthe periphery of the gas production and distribution environment 105, asshown by client 125E. In some embodiments, the client 125E can beconfigured as a remote weather station capable of transmitting weatherdata to another of clients 125 or to the emission analyzer 135. TheFar-Field sensors, clients 125A, 125C, and/or 125E, can be preferablypositioned to maximize detection of gas emissions based on the directionof the seasonally prevailing wind conditions (W).

In quadrant 110B, client 125B can be configured with respect to emissionsource 115B, such as a vent associated with a compressor. The emissionsource 115B vents gas as part of the normal operating behavior of thecompressor. Thus, the absence or sudden change in the gas emission 120Bmay indicate a failure of the emission source 115B (e.g., the vent), orthe equipment associated with the emission source, such as thecompressor. The client 125B can be configured as a Near-Field sensor todetect the gas 120B emanating from the emission source 115B. As aNear-Field sensor, client 125B can be positioned in proximity to apotential emission source 115B. For example, the client 125B can beplaced less than 1 foot away, less than 2 feet away, or less than 5 feetaway from the potential emission source 115B. In some embodiments, theNear-Field sensor, client 125B, can be positioned with respect to anemission source 115B, which can be operating under high operationalpressures. In some embodiments, the Near-Field sensor or client 125B,can be configured with an arrangement of baffles in order to minimizethe advection-diffusion effect of the local wind conditions, W.Similarly, in quadrant 110D, client 125D can be configured as aNear-Field sensor in proximity with emission source 115D. For example,emission source 115D can be a high-pressure, methane storage system thatis configured to routinely vent excess methane 120D from the storagesystem. In this example, the client 125D can include a Near-Field sensorconfigured to monitor and detect the methane emission 120D.Additionally, client 125F can be configured as a Near-Field sensor thatcan be deployed on a mobile platform, such as a transport truck used tocarry and distribute CNG or LNG gas. The Near-Field sensor configured asclient 125F can be positioned with respect to the mobile platform so asto monitor and detect a gas leak that may emanate from one or more ofthe gas transport devices included in the mobile platform. Additionaldetails regarding the configuration of Near-Field sensors with respectto mobile platforms will be provided in relation to FIG. 3 .

As shown in FIG. 1 , the clients 125 can be configured to share datawith other clients 125. For example, as shown in FIG. 1 by dashed linesbetween clients 125A and 125 C and between clients 125A and 125B, theclients 125 can transmit and receive data with other clients 125 so asto form a sensor grid, a network of clients 125, or the like. Theclients 125 can further be operatively coupled via a network 130 to anemission analyzer 135. The emission analyzer 135 can receive the sensordata from clients 125 as Near-Field sensor data, such as from clients125B, 125D, and/or 125F. In addition, the emission analyzer 135 canreceive the sensor data from clients 125 as Far-Field sensor data, suchas from clients 125A, 125C, or 125E. The emission analyzer 135 can beconfigured as a client computing device, or as a server computingdevice. In some embodiments, one or more of the modules can be locatedremotely from the location of the emission analyzer 135. The emissionanalyzer 135 can perform different processing functions in regard to gasemission monitoring and detection. For example, the emission analyzercan include a data filter module 140 configured to receive sensor and/orweather data and to detect parser errors, incorrect or anomalous datavalues, dates, or sensor measurements. In some embodiments, the datafilter module can apply global mining to filter out data that liesoutside of a sensor calibration data range associated with at least oneof the clients 125. The emission analyzer 135 can also include aprediction module 145 configured to predict an emission rate for aparticular gas emission 120 based gas concentration data converted fromthe filtered sensor data. The emission analyzer 135 can also include anoptimization module 150 configured to perform data-driven modeling,machine learning, and statistical analysis of the gas emission 120. Insome embodiments, the optimization module 150 can be configured in amachine learning process to perform the data-drive modeling andstatistical analysis of the gas emission 120. The emission analyzer 135can also include a control module 155 which can be configured to controlone or more emission sources 115 or components associated the withemission sources 1115. In the event of determining anomalous venting orfugitive gas emissions 120, the control module 155 can executeinstructions to control the operating parameters of the emission sources115 via client 125. The emission analyzer 135 can further include amaintenance module 160 which can be configured to request, manage, andallocate maintenance and repair personnel in response to anomalousventing or fugitive gas emissions 120. For example, based on determiningthat rate of gas from emission source 115A is outside of the normaloperating conditions, the emission analyzer 135 can execute instructionscausing the maintenance module to provide emission data regarding theanomalous behavior of emission source 115A to maintenance personnelassociated with the gas production and distribution environment 105 inorder for corrective action to be applied to the emission particularemission source.

FIG. 1B illustrates an example of Far-Field and Near-Field sensors as aclient grid 100B where each sensor node is denoted by 1 through 13. Theemission source 115 or leak source is shown at one location within theclient grid as a star shaped icon. In one embodiment, the client grid isdivided into four quadrants (I-IV). The Emission analyzer 135 willdetermine emissions under prevailing wind conditions for each sensornode and based on magnitude of emissions for each sensor node, it willselect nodes 2, 3, 4, 13, and 10 being closest to the emission source115 (the star icon) for various wind conditions. The next step would beto further collect data for the quadrant II and subdividing thisquadrant into four quadrants (I-IV). Based on additional client datafrom nodes 2, 3, 4, 13 and 10, nodes 3 and 10 are selected as closest tothe emission source 115. The next step is to select the quadrant II withclient nodes as 3 and 10 in the closest vicinity of emission source 115and also to estimate the leak rate based on diffusion/advection underprevailing wind conditions. This process of creating a client grid ofcertain dimensions (X, Y, Z) containing the emission source 115 and thenusing a process of elimination to first eliminate quadrants followed byeliminating client nodes 125 indicating zero or negligible emissions canbe used to provide a structured approach to detecting the rate andlocation of leaks from emission sources 115.

The process for selecting the client grid dimensions (X, Y, Z) aroundthe leak source(s) is shown in more detail in FIGS. 1C, 1D, and 1E. Acomputational model is constructed as shown in FIG. 1C to estimate fluiddispersion under known leak rate and known wind direction and windvelocity. As shown in FIG. 1D, under wind velocity of 0.5 m/s, formethane leak rate of 0.5 g/s, the sensor should be placed less than ˜40ft far from the leak source. This can help the sensor to detect andmeasure methane concentration above ˜2 ppm which is the lowest detectionlimit for the sensor considered here as an example. As shown in FIG. 1E,if the wind velocity is increased to 2 m/s and 5 m/s, same 0.5 g/s leakrate, due to faster dispersion, the plume dissipates quickly and isdetectable at lower distances from the leak source at ˜2 ppm methaneconcentration.

FIGS. 2A-2B are block diagrams illustrating a client device for use in asystem for monitoring and detecting gas emissions. FIG. 2A is a blockdiagram illustrating a client computing device that can be configured asa Far-Field sensor, such as client 125A as shown and described inrelation to FIG. 1 . FIG. 2B is a block diagram illustrating a clientcomputing device that can be configured as a Near-Field sensor, suchclient 125B as shown and described in relation to FIG. 1 .

Client 125A configured as a Far-Field sensor can be located 10 to 200feet away from a potential gas emission source 115. In some embodiments,the Far-Field sensor can be positioned 4 to 8 feet above the ground. Insome embodiments, the Far-Field sensor can be positioned on theperiphery or battery limits of the gas production and distributionenvironment 105. The Far-Field sensors, clients 125A for example, arepreferably positioned to maximize the capture of gas emissions 120 andmay be configured downstream of seasonally prevalent winds.

As shown in FIG. 2A, the client 125A configured as a Far-Field sensorcan include a wireless communication transceiver 205. The wirelesscommunication transceiver 205 can transmit and receive data with one ormore clients 125 and with the emission analyzer 135. In someembodiments, the client 125A may also include or may alternativelyinclude a wired communication interface (not shown). The wirelesscommunication transceiver 205 can enable wireless data transmission ofthe Far-Field sensor data generated by the client 125.

As further shown in FIG. 2A, the client 125A configured as a Far-Fieldsensor can also include a solar panel 210. The solar panel 210 canprovide a source of power for the client 125A based on converting solarenergy received from the sun into electrical energy. In someembodiments, the client 125A can also include or alternatively include aconfiguration to receive power from a nearby AC power source, such as anelectrical power grid or the like.

As further shown in FIG. 2A, the client 125A configured as a Far-Fieldsensor can include a Far-Field wind sensor 215. The Far-Field windsensor 215 can include one or more sensors configured to measure a windvelocity and a wind direction. Far-Field wind sensor 215 can generatesensor data as time-series data associated with wind velocity and winddirection sensed by the Far-Field wind sensor 215 over a period of time.The time-series wind data can include data values collected every 2seconds, every hour, every day, or every week. In some embodiments, thetime-series wind data can be averaged. In some embodiments, theFar-Field sensor can include additional weather sensors which maymeasure ambient pressure, temperature, dew point, humidity,precipitation, and solar radiation.

The client 125A configured as a Far-Field sensor can also include aFar-Field gas sensor 220. The Far-Field gas sensor 220 can include oneor more sensors configured to measure the concentration of a gas. Forexample, the Far-Field gas sensor 220 can be configured to measure theconcentration of methane which may be present within the measurementproximity of the client 125A. Far-Field gas sensor 220 can generatesensor data as time-series data associated with gas concentrationssensed by the Far-Field gas sensor 220 over a period of time. Thetime-series gas concentration data can include data values collectedevery 2 seconds, every hour, every day, or every week. In someembodiments, the time-series gas concentration data can be averaged. Insome embodiments, the Far-Field gas sensor 220 can detect common gasspecies including methane, ethane, propane, butane, hexane, and otherhydrocarbons such as natural gas liquids (C5, C6, C8-10), mixtures ofalkanes, sour gases include H2S, SOx, carbon disulfide, unsaturatedHCs/petrochemicals such as ethylene, propylene, etc.

As further shown in FIG. 2A, the client 125A also includes a battery225, a processor 230, a memory 235, and a communications interface 240.In some embodiments, the battery 225 can receive and store powergenerated by the solar panel 210. The processor 230 can executecomputer-readable, executable instructions stored in memory 235 whichwhen executed cause the client 125 to record sensor data received fromthe Far-Field wind sensor and/or the Far-Field gas sensor 220 and tostore the sensor data in the memory 235. In some embodiments, theprocessor 230 can execute instructions to cause the client 125 totransmit the sensor data via the communications interface 240 to anotherclient 125, and/or to the emissions analyzer 135.

As shown in FIG. 2B, client 125B can be configured as a Near-Fieldsensor. As a Near-Field sensor, client 125B can be located in closeproximity to a potential gas emission source 115. For example, in someembodiments, the Near-Field sensor can be positioned less than 5 feetaway from a potential gas emission source and preferably less than 1foot away from the potential gas emission source. In some embodiments,the Near-Field sensor can be positioned 6 to 10 feet away from thepotential gas emission source. The Near-Field sensors, clients 125B forexample, are preferably positioned to maximize the capture of gasemissions 120 from an emission source 115 operating under high pressureconditions.

As shown in FIG. 2B, the client 125B configured as a Far-Field sensor ora Near-Field sensor can include a wireless communication transceiver205. The wireless communication transceiver 205 can transmit and receivedata with one or more clients 125 and with the emission analyzer 135. Insome embodiments, the client 125B may also include or may alternativelyinclude a wired communication interface (not shown). The wirelesscommunication transceiver 205 can enable wireless data transmission ofthe Near-Field sensor data generated by the client 125.

As further shown in FIG. 2B, the client 125B configured as a Near-Fieldsensor can also include a solar panel 210. The solar panel 210 canprovide a source of power for the client 125B based on converting solarenergy received from the sun into electrical energy. In someembodiments, the client 125B can also include or alternatively include aconfiguration to receive power from a nearby AC power source, such as anelectrical power grid or the like.

As further shown in FIG. 2B, the client 125B configured as a Near-Fieldsensor can include a Near-Field wind sensor 250. The Near-Field windsensor 215 can include one or more sensors configured to measure a windvelocity and a wind direction. Near-Field wind sensor 250 can generatesensor data as time-series data associated with wind velocity and winddirection sensed by the Near-Field wind sensor 250 over a period oftime. The time-series wind data can include data values collected every2 seconds, every hour, every day, or every week. In some embodiments,the time-series wind data can be averaged. In some embodiments, theNear-Field sensor can include additional weather sensors which maymeasure ambient pressure, temperature, dew point, humidity,precipitation, and solar radiation.

The client 125B configured as a Near-Field sensor can also include aNear-Field gas sensor 255. The Near-Field gas sensor 255 can include oneor more sensors configured to measure the concentration of a gas. Forexample, the Near-Field gas sensor 255 can be configured to measure theconcentration of methane which may be present within the measurementproximity of the client 125B. Near-Field gas sensor 255 can generatesensor data as time-series data associated with gas concentrationssensed by the Near-Field gas sensor 255 over a period of time. Thetime-series gas concentration data can include data values collectedevery 2 seconds, every hour, every day, or every week. In someembodiments, the time-series gas concentration data can be averaged. Insome embodiments, the Near-Field gas sensor 255 can detect common gasspecies including methane, ethane, propane, butane, hexane, and otherhydrocarbons such as natural gas liquids (C5, C6, C8-10), mixtures ofalkanes, sour gases include H2S, SOx, carbon disulfide, unsaturatedHCs/petrochemicals such as ethylene, propylene, etc.

As shown in FIG. 2B, the client 125B configured as a Near-Field sensorcan also include one or more baffles 245. The baffles 245 can beconfigured with respect to the client 125B and the components includedtherein so as to minimize wind advection-diffusion effects. The bafflearrangement 245 assists in maintain the Near-Field wind speed at 5 mphor less. It is preferable to position the Near-Field sensors upstreamfrom the prevailing winds so that the angle between a vectorrepresenting the wind direction and a vector representing the distancefrom the leak source to the sensor is no more than five degrees.

As further shown in FIG. 2B, the client 125B also includes a battery225, a processor 230, a memory 235, and a communications interface 240.In some embodiments, the battery 225 can receive and store powergenerated by the solar panel 210. The processor 230 can executecomputer-readable, executable instructions stored in memory 235 whichwhen executed cause the client 125 to record sensor data received fromthe Near-Field wind sensor and/or the Near-Field gas sensor 220 and tostore the sensor data in the memory 235. In some embodiments, theprocessor 230 can execute instructions to cause the client 125 totransmit the sensor data via the communications interface 240 to anotherclient 125, and/or to the emissions analyzer 135.

FIGS. 3A-3C are block diagrams illustrating one exemplary embodiment ofa mobile platform including for use in in a system for monitoring anddetecting gas emissions. As shown in FIG. 3A, a mobile platform 305,such as a CNG transport truck or a LNG transport truck can be configuredto transport one or more tanks or containers 310 of a gas. The tanks 310can terminate in and be accessed through a piping control cabinet 315that is configured at the rear of the truck. One or more Near-Fieldclients can be configured in relation to the cabinet 315 in order todetect potential gas emissions that may emanate from around one or moreof the tanks 310 and/or from one or more components associated with thetanks 310. For example, as shown in FIG. 3A, the truck 305 includes afirst client 325A, configured as a Near-Field sensor that is positionedoutside of the cabinet 315. In addition, the truck 305 includes a secondclient 325B, also configured as a Near-Field sensor that is positionedwithin the cabinet 315.

Configuring one or more clients 325 as Near-Field sensors positionedwith respect to a mobile platform 305 provides advantages of detectingsmall leaks at the parts-per-million (ppm) scale that may emanate frominside the cabinet 315. Additionally, configuring one or more clients325 as Near-Field sensors outside of the cabinet 315 is advantageous todetect over pressure leaks that may occur when a pressure relief deviceis activated. The Near-Field sensors can be configured in relation toalarms and/or leak flow recorders. The Near-Field sensors can furtheroutput data to mobile computing device, such as a smartphone or tabletwith a user interface that may allow a driver of the truck to check theintegrity status of one or more containers 310. The Near-Field sensorscan be employed during loading or dispensing of gas (CNG) or liquid(LNG) at filling stations. In some embodiments, the clients 325configured as Near-Field sensors with respect to a mobile platform canbe configured to automatically call the nearest fire department and/orpolice using a wireless communication device 205 when a pressure reliefdevice is activated. In this way, nearby communities or highways may beclosed to reduce the risk of potential gas emissions associated with thetanks 310 of the mobile platform. In some embodiments, the clients 325configured as Near-Field sensors can include an auxiliary power source,such as battery 225, the can provide continuous operation to the client325 when the truck is parked or stationary.

FIG. 3B illustrates a configuration of tanks 310 as viewed from the rearof the truck 305. Client 325A can be configured within the cabinet 315and client 325B can be configured inside the cabinet 315. TraditionalLNG and CNG transport trailers are not equipped with active sensordevices configured to perform gas monitoring and detection due to lackof integral gas sensing and analytics model which can be used formeasuring emission concentrations and further determining emissionrates. As shown in FIG. 3C, each tank 310 includes a pressure reliefdevice 320 that is routed to a top section of the cabinet 315 usingpiping 330. Client 325B can be configured within the cabinet 315 todetect emission leaks generated by the threaded connections coupling thepressure relief device to a tank 310. Client 325A can configured outsideof the cabinet 315 to detect larger gas emissions that may occur whenthe pressure relief devices 320 on one or more tanks 310 are activated.

While the clients 325 are described as Near-Field sensors configured ona LNG or CNG transport truck 305, the clients 325 can also be configuredon a variety of mobile platforms including rail cars or tankers and alsoincluding the mobile platforms which are not necessarily associated withthe transport of a gas. For example, the clients 325 can be configuredon a manned or unmanned ground vehicle capable of maneuvering to apotential gas emission source and collecting Near-Field sensor data at alocation proximal to the emission source. In some embodiments, theclients 325 can be configured on a drone or a robot or on a mobileplatform which can be attached to a human in motion. The Near-Fieldsensor data can be used to determine an emission rate associated withthe emission source.

FIG. 4 is a block diagram illustrating one exemplary embodiment of anarchitecture of a system for monitoring and detecting gas emissions. Asshown in FIG. 4 , the system 400 includes one or more clients 125coupled to an emission analyzer 135 via a network 130. The clients 125can include one or more client computing devices configured as eitherFar-Field sensors or Near-Field sensors as described in relation toFIGS. 1-3 . The clients 125 can be, for example, a large-formatcomputing device, a small-format computing device (e.g., a smartphone ortablet), or any other similar device having appropriate processor,memory, and communications capabilities to transmit sensor and/orweather data. The clients 125 can be configured to receive, transmit,and store sensor data and/or weather data associated with determining anemission rate from a gas emission source. The clients 125 can beconfigured with one or more software applications. The softwareapplications can include web-based applications as well as applicationsthat can be directly hosted or configured on the clients 125. Forexample, the software applications can include technical computingapplications, modeling and simulation applications, sensor control andconfiguration applications, emission data processing applications, andindustrial asset management applications, or the like.

In some embodiments, the client devices 125 can further include aweather station configured with a plurality of weather sensing devicesused to measure ambient pressure, temperature, wind speed, winddirection, humidity, and solar radiation. In some embodiments the clientdevices 125 can include a mobile computing device, such as a smart phoneor table computing device, which may be configured to receive andprovide sensor data, weather data, emission rates, emission data, or thelike.

As shown in FIG. 4 , the system 400 includes a network 130. The network130 can include, for example, any one or more of a personal area network(PAN), a local area network (LAN), a campus area network (CAN), ametropolitan area network (MAN), a wide area network (WAN), a broadbandnetwork (BBN), a virtual private network (VPN), the Internet, or thelike. Further, the network 130 can include, but is not limited to, anyone or more of the following network topologies, including a busnetwork, a star network, a ring network, a mesh network, a star-busnetwork, tree or hierarchical network, and the like. In someembodiments, the network 130 can be a grid or sensor network formed bytwo or more clients 125.

As further shown in FIG. 4 , the system 400 includes an emissionanalyzer 135 configured with a plurality of modules for determining anemission rate associated with a gas leak based on sensor data includingweather data and gas concentration data. In some embodiments, one ormore of the modules configured in the emission analyzer 135 can beconfigured on a server computing device. In some embodiments, one ormore of the modules can be configured on a client device, such as clientdevices 125, without deviating from the spirit of the disclosuredescribed herein.

The emission analyzer 135 includes a data filter module 140. The datafilter module 140 can be configured to receive sensor data astime-series datasets from Near-Field sensors and Far-Field sensors andto filter out unusable data from the time-series datasets. The sensordata can include time-series datasets of weather data and gasconcentration data collected by the Near-Field sensors and the Far-Fieldsensors. The data filter module 140 can receive the sensor data andautomatically detect parse errors, anomalous sensor data values,incorrect dates or times, or the like. The data filter module 140 canfurther apply global mining to filter out data that lies outside of asensor calibration data range associated with at least one of theclients 125.

As shown in FIG. 4 , the emission analyzer 135 also includes aprediction module 145. The prediction module 145 can be configured toconvert filtered gas sensor data received from the data filter module140 to gas concentration data. The prediction module 140 can apply atransfer function to convert the filtered gas sensor data to generategas concentration data.

The prediction module 140 can be further configured with a Near-Fieldadvection-diffusion model expressed by equation 1 below used todetermine the emission rate of a gas emission source:

$\begin{matrix}{q_{0} = {{C\left( {4\;\pi\; K\; d} \right)}{\exp\left\lbrack {{- \frac{1}{2\; K}}{\overset{\_}{v}}{d\left( {1 - {\cos\;\theta}} \right)}} \right\rbrack}}} & (1)\end{matrix}$

As shown in equation 1, “C” represents the concentration of a gas in ppm(or mg/m³). “d” represents the distance between a sensor and an emissionsource 115 in meters. “K” represents a diffusivity constant in m²/s.“∥v∥” represents the wind speed in meters per second, m/s. “θ”represents the angle between the wind vector and the distance vector aspointed from the leak source to the sensor in radians. “q₀” representsthe emission rate in m³/s.

In predicting gas emission rates using Far-Field sensor data, theFar-Field wind data received from a client 125 configured as a Far-Fieldsensor and sensor location shape factors can be provided as inputs to aFar-Field model in order to generate Near-Field wind data. TheNear-Field wind data and gas concentration data can then be provided asinputs to the Near-Field dispersion model described above in equation(1) to predict a gas emission rate. In some embodiments, the Far-Fieldmodel can be generated via reduced order modeling and/or via a machinelearning model that has been trained using computational fluid dynamic(CFD) simulation datasets. The CFD simulation datasets represent windvector datasets including wind velocity fields on selected Far-Fieldwind conditions, such as boundary conditions for a particular CFDsimulation. The CFD simulation datasets also include bluff body shapefactors that represent typical oil production facility object shapes. Insome embodiments, the object shapes can be a cube, a horizontalcylinder, a vertical cylinder, or the like.

As further shown in FIG. 4 , the emission analyzer 135 can also beconfigured to include an optimization module 150. The optimizationmodule 150 can implement a polling process that will access publicweather forecasting data identifying weather parameters such asprecipitation probability, precipitation forecast, probability ofextreme thunderstorms or high winds, relative humidity, temperature,wind speed, and wind direction to determine whether sensor data shouldbe collected from the Near-Field and Far-Field sensors in a futureperiod of time, such as within the next 12 hours. The optimizationmodule 150 can further determine when the optimum time for polling orsensor data collection will occur. By recognizing patterns of windturbulence from the weather forecasting data, the optimization module150 can determine when weather conditions are unfavorable to receivesensor data. The optimization module 150 can determine favorable sensorpolling times by calculating a Fourier spectrum of wind speed andestimating a frequency/timescale contrasting acceptable (e.g.,relatively slow timescales) and unacceptable conditions (e.g.,relatively fast timescales). In this way, the optimization module 150can be configured to instruct a client device 125 to access localweather forecasting data while other nodes sleep or are otherwiseinactive and can determine a window of time to conduct polling whennoise in the wind sensor data is at a minimum. In some embodiments, theoptimization module 150 can access the local weather forecasting data.The optimization module 150 can be configured to determine futurepolling windows based on the occurrence of a previously successfulpolling window. For example, the optimization module 150 can beconfigured to allow for multiple polls within an upcoming 12 hour periodwhen polling conducted in a previous amount of time provided noise-freeor noise-reduced wind data which may result from high winds, rain, orsevere weather. In the event of a connection failure with a source ofthe public weather forecasting data, the optimization module 150 cancause one or more clients 125 to observe wind direction and speed and topoll for sensor data for a test period, such as one minute, and to haltpolling in the presence of noise in the received data.

The optimization module 150 can be further be configured to post-processthe gas emission rate data output from the prediction module 145 using avariety of statistical modeling methods, analytics, clusteringtechniques, and visualizations. For example, the optimization module 150can apply various statistical modeling methods such as pdf, BS, C.I, andsimulation. In some embodiments, timeseries data from individual sensorscan be streamed to Amazon Web Servers (AWS) in real-time. The data canbe comprised of raw sensor signal as it responds to local methaneconcentration at the location of the sensor and wind speed and directionmeasurements of the edge device. Sensor data sampled can be pushed toAWS every hour in a single file that requires preprocessing beforegetting passed on to the Bayesian inferencing model for leak rate andsource location prediction. Data can be downloaded into local servers,and can be passed on to an extraction, transformation and loading (ETL)computational pipeline before being ready for the prediction algorithm.First data from all sensors can be checked for missing values andimputed where appropriate. Typically, a full dataset can be acquiredunless data connectivity gets interrupted and nodes stop streaming data.Such occurrences are rare but need to be considered in any ETL. Afterdata imputing for individual sensors, timeseries need to be synchronizeddue to slight variations in the time (typically less than 1 second) ofmeasurements reported by the individual sensors. Once raw data isimputed and aligned it can be transformed into methane concentration inPPM using transfer functions. Finally, the concentration data (in PPM)can be augmented by GPS coordinates of the individual sensors and asingle file encompassing the experimental time of a given experiment(typically 1 hour long) can be provided to a Bayesian inferencing model.Bayesian inversion relies on a forward model for pollutant dispersion,such as Gaussian-plume and other reduced order numerical models. Dataverification and validation are essential parts of the ETL pipeline. APython basemap (a matplotlib library extension) of the experimentalsetup can be displayed so we could visually verify relative sensor andsource locations of a given experimental setup. This way GPS coordinatescan be correctly paired with their respective sensor nodes. Anyconnectivity issues, signifying a node or more getting out of sync withrespect to the others, can be detected programmatically by raisingexceptions in case a data gap is found within the source files.

Additionally, or alternatively, the optimization module 150 can processthe gas emission rate data and can apply various analytic tools such asopen-source python-based libraries to construct the data pre-processing(ETL) and Statistical computing and visualization (SV) pipelines.

A summary of the different libraries along with short description isshown below in Table 1.

TABLE 1 Summary of Python Libraries used for the ETL, SV PipelinesLibrary Name Description Pymc3 For Bayesian statistical modeling TheanoFor linear algebra and matrix computing Pandas, Numpy, Manipulation oftimeseries data, performing Scipy database like functionality (grouping,joining . . . etc.) and common timeseries operations (resampling,shifting, slicing, rolling means, etc.) Matplotlib, Scipy Forvisualization of timeseries, histograms, (stats), and Seabornprobability/kernel density functions Os, shutil, pickle, Variousutilities to move data around the json operating system, read/load andsave data files numpy.random.choice For sampling a timeseries withreplacement to construct a bootstrap

In some embodiments, the optimization module 150 can process the gasemission rate data to perform various clustering optimizations in orderto determine categories of emission rates as low/medium/high based onclassifications of the gas emission rate data. Clustering methods usedcan include manipulation of timeseries data, performing database-likefunctionality such as grouping, joining, or the like, and performingcommon time-series data operations such as resampling, shifting,slicing, rolling means, or the like using Pandas, Numpy, or Scipy, asshown in Table 1 above.

Additionally, or alternatively, the optimization module 150 can beconfigured to generate a variety of visualizations based on the gasemission rate data. For example, the optimization module 150 cangenerate visualizations which assign a signature or finger print to eachgas emission based on the determined emission rate data. Forvisualization of time-series data, histograms, probability/kerneldensity functions libraries such as Matplotlib, Scipy (stats), andSeaborn shown in Table 1 above can be used.

The emission analyzer 135 can also include a memory 165. The memory 165can store and provide computer-readable executable instructions whichwhen executed cause the one or more modules to perform functionality asdescribed above. In some embodiments, the memory 165 includes aplurality of machine learning models and training data used to train theFar-Field model. The memory 165 can also store various time-seriesdatasets associated with Far-Field wind data, Far-Field gasconcentration data, Near-Field wind data, and Near-Field gasconcentration data. In some embodiments, the memory 165 can store one ormore rules or threshold values used in determining alarm statesassociated with particular gas concentrations, emission rates, weatherdata, and/or emission sources.

FIG. 5 is a flowchart illustrating one exemplary embodiment of a methodfor monitoring and detecting gas emissions using the system of FIG. 4 .In operation 505, Near-Field sensor data is received by the emissionanalyzer 135 from one or more clients 125 configured as Near-Fieldsensors. In operation 510, Far-Field sensor data is received by theemission analyzer 135 from one or more clients 125 configured asNear-Field sensors. In operation 515, the received sensor data isfiltered by the data filter module 140 as described above in relation toFIG. 4 . The data filter module 140 can perform various processingmethods to cleanse the data or otherwise remove sensor data values whichmay be inaccurate or anomalous.

In operation 520, the prediction module 145 receives the filtered sensordata and determines gas concentration data. The gas concentration datacan be determined by applying a transfer function to the filtered sensordata received from Near-Field and Far-Field sensors to determine the gasconcentration data.

In operation 525, the prediction module 145 receives the filtered windsensor data from Far-Field sensors and/or client devices 125 that can beconfigured as weather stations and maps the wind sensor data to sensorlocation wind conditions. To derive the relationship between thenear-field velocity vector and the far-field sensor measurements, adata-driven modeling approach can be devised. This data-driven model canestimate the near-field wind information to be used in the dispersionmodel based on the data measured at the far-field wind sensor locations.To build such a model that can operate over a wide range of windconditions, CFD models of the flow around a subset of representativeinfrastructures and different values of the wind vector (wind speed anddirections) can be discretized over typically observed ranges of values.For example, the velocity magnitude and wind values can be parameterizedover a representative range of 0.5-9.5 m/s and 0-360 degrees (every 30degrees) for a wind sensor placed at a height of five feet. Further, tobuild this dataset, we assumed a square infrastructure of a standardsize of 15 feet. From this ensemble of simulations, a database of inputand output features corresponding to open (far) field and near-fieldvelocity measurements can be generated for training a machine learning(ML) model. The ML architecture aims to learn a mapping in feature spaceusing an extended basis consisting of polynomials up to order two whichis similar to a shallow neural network or a single layer feed forwardneural network (SLFNN). We note that learning more complex hierarchicalmodels such as that using a deep neural network (DNN) is equallyplausible but were avoided for simplicity. The data was split intotraining and validation datasets in a ratio of 4:1.

In operation 530, the prediction module 145 determines the emission rateassociated with the received sensor data corresponding to a particularemission source. The emission rate can be determined using a Near-Fielddispersion model. The Near-Field dispersion model can receive as inputsthe time-series gas concentration data generated by Near-Field sensorsand the Near-Field time-series wind data converted from Far-Field winddata generated by Far-Field sensors. The Far-Field wind data generatedby the Far-Field sensors can be input to a Far-Field model configured togenerate Near-Field wind data. The Far-Field model can receive as inputsFar-Field wind data that may be received from one or more Far-Fieldsensors, of that may be streamed from a remote server accessible vianetwork 130. The Far-Field model can further receive as inputs sensorlocation shape factors which can be determined based on sensorinstallations and their location. The Near-Field dispersion model canprocess the inputs to determine an emission rate for a particularemission source in standard cubic feet per minute (SCFM).

In operation 535, the prediction optimization module 150 can generateemission data including the determined emission rate and one or moresource locations associated with the gas emission. In some embodiments,the gas emission may not be associated with a source location. In someembodiments, the source locations can be potential source locations ofthe emission. The emission data can be further processed to generateadditional representations of the emission rate. For example, theoptimization module 150 can perform statistical modeling, applyexploratory analytics, evaluation measures, and/or clustering algorithmsor methods to determine categories of emission data or emission sourcesbased on classification techniques, as well as generating visualizationsof the emission data such as emission rate signatures, emission sourcesignatures, or the like.

In operation 540, the emission analyzer 135 can provide the emissiondata to one or more clients 125. The client device 125 can be configuredto receive the emission data and provide it for display or store it in amemory configured on the clients 125. In some embodiments, the emissiondata can be provided to a control module configured to execute controlinstructions which can alter the operation of an emission source basedon alarm conditions which may be associated with the emission data.

FIG. 6 is a block diagram of an exemplary computing device 610 suitablefor use in the gas monitoring and detection system of FIGS. 1 and 4 .

In broad overview, the computing device 610 includes at least oneprocessor 650 for performing actions in accordance with instructions,and one or more memory devices 660 and/or 670 for storing instructionsand data. The illustrated example computing device 610 includes one ormore processors 650 in communication, via a bus 615, with memory 670 andwith at least one network interface controller 620 with a networkinterface 625 for connecting to external devices 630, e.g., a computingdevice (such as client 125, emission analyzer 135, or the like). The oneor more processors 650 are also in communication, via the bus 615, witheach other and with any I/O devices at one or more I/O interfaces 640,and any other devices 680. The processor 650 illustrated incorporates,or is directly connected to, cache memory 660. Generally, a processorwill execute instructions received from memory. In some embodiments, thecomputing device 610 can be configured within a cloud computingenvironment, a virtual or containerized computing environment, and/or aweb-based microservices environment.

In more detail, the processor 650 can be any logic circuitry thatprocesses instructions, e.g., instructions fetched from the memory 670or cache 660. In many embodiments, the processor 650 is an embeddedprocessor, a microprocessor unit or special purpose processor. Thecomputing device 610 can be based on any processor, e.g., suitabledigital signal processor (DSP), or set of processors, capable ofoperating as described herein. In some embodiments, the processor 650can be a single core or multi-core processor. In some embodiments, theprocessor 650 can be composed of multiple processors.

The memory 670 can be any device suitable for storing computer readabledata. The memory 670 can be a device with fixed storage or a device forreading removable storage media. Examples include all forms ofnon-volatile memory, media and memory devices, semiconductor memorydevices (e.g., EPROM, EEPROM, SDRAM, flash memory devices, and all typesof solid state memory), magnetic disks, and magneto optical disks. Acomputing device 610 can have any number of memory devices 670.

The cache memory 660 is generally a form of high-speed computer memoryplaced in close proximity to the processor 650 for fast read/writetimes. In some implementations, the cache memory 660 is part of, or onthe same chip as, the processor 650.

The network interface controller 620 manages data exchanges via thenetwork interface 625. The network interface controller 620 handles thephysical, media access control, and data link layers of the Open SystemsInterconnect (OSI) model for network communication. In someimplementations, some of the network interface controller's tasks arehandled by the processor 650. In some implementations, the networkinterface controller 620 is part of the processor 650. In someimplementations, a computing device 510 has multiple network interfacecontrollers 620. In some implementations, the network interface 625 is aconnection point for a physical network link, e.g., an RJ 45 connector.In some implementations, the network interface controller 620 supportswireless network connections and an interface port 625 is a wirelesstransceiver. Generally, a computing device 610 exchanges data with othernetwork devices 630, such as computing device 630, via physical orwireless links to a network interface 625. In some implementations, thenetwork interface controller 620 implements a network protocol such asLTE, TCP/IP Ethernet, IEEE 802.11, IEEE 802.16, or the like.

The other computing devices 630 are connected to the computing device610 via a network interface port 625. The other computing device 630 canbe a peer computing device, a network device, or any other computingdevice with network functionality. For example, a computing device 630can be a (client device 125 configured as a Near-Field sensor device,client device 125 configured as a Far-Field sensor device, emissionanalyzer 135, or the like) which may be configured within the gasmonitoring and detection system illustrated in FIG. 1 . In someembodiments, the computing device 630 can be a network device such as ahub, a bridge, a switch, a relay, or a router, connecting the computingdevice 610 to a data network such as a LAN, a WAN, the Internet, and/ora virtual private network.

In some uses, the I/O interface 640 supports an input device and/or anoutput device (not shown). In some uses, the input device and the outputdevice are integrated into the same hardware, e.g., as in a touchscreen. In some uses, such as in a server context, there is no I/Ointerface 640 or the I/O interface 640 is not used. In some uses,additional other components 680 are in communication with the computersystem 610, e.g., external devices connected via a universal serial bus(USB).

The other devices 680 can include an I/O interface 640, external serialdevice ports, and any additional co-processors. For example, a computingdevice 610 can include an interface (e.g., a universal serial bus (USB)interface, or the like) for connecting input devices (e.g., a keyboard,microphone, mouse, or other pointing device), output devices (e.g.,video display, speaker, refreshable Braille terminal, or printer), oradditional memory devices (e.g., portable flash drive or external mediadrive). In some implementations an I/O device is incorporated into thecomputing device 610, e.g., a touch screen on a tablet device. In someimplementations, a computing device 610 includes an additional device680 such as a co-processor, e.g., a math co-processor that can assistthe processor 650 with high precision or complex calculations.

The improved plume prediction system described herein addresses thetechnical problem of determining an emission rate of a gas emissionemanating from a gas source based on received sensor data. Determiningand generating accurate emission rates for different types of emissionsources can be difficult and exacerbated by prevailing weatherconditions. The exemplary technical effects of the methods, systems,devices, and computer-readable mediums described herein include, by wayof non-limiting example, determining emission rates for expected, ventedemissions as well as unexpected, fugitive emissions which may emanatingfrom emission sources within a gas and production environment. Emissionrates can be determined using a Near-Field dispersion model configuredto receive wind and gas concentration data from Near-Field sensors. TheNear-Field dispersion model can further be configured to receiveFar-Field wind and/or gas concentration data as well as sensor locationshape factors associated with objects within the gas production anddistribution environment which may be located in proximity of theNear-Field or Far-Field sensor installations.

Thus the system represents an improvement of computer functionality thatprocesses sensor data and generates emission rates and emission datacorresponding to one or more types of emissions from an emission source.Additionally, the clients 125 can include an improved display orgraphical user interface (GUI) that provides more efficientvisualization and execution of emission data such as when visualizingthe location and source of emissions. The improved GUI can also provideenhanced visualizations for alerts or notifications of gas emissions,planning maintenance or repair procedures for emission sources, or formanaging production rates of the gas production and distributionenvironment within desirable ranges.

Certain exemplary embodiments have been described to provide an overallunderstanding of the principles of the structure, function, manufacture,and use of the systems, devices, and methods disclosed herein. One ormore examples of these embodiments have been illustrated in theaccompanying drawings. Those skilled in the art will understand that thesystems, devices, and methods specifically described herein andillustrated in the accompanying drawings are non-limiting exemplaryembodiments and that the scope of the present invention is definedsolely by the claims. The features illustrated or described inconnection with one exemplary embodiment may be combined with thefeatures of other embodiments. Such modifications and variations areintended to be included within the scope of the present invention.Further, in the present disclosure, like-named components of theembodiments generally have similar features, and thus within aparticular embodiment each feature of each like-named component is notnecessarily fully elaborated upon.

The subject matter described herein can be implemented in analogelectronic circuitry, digital electronic circuitry, and/or in computersoftware, firmware, or hardware, including the structural meansdisclosed in this specification and structural equivalents thereof, orin combinations of them. The subject matter described herein can beimplemented as one or more computer program products, such as one ormore computer programs tangibly embodied in an information carrier(e.g., in a machine-readable storage device), or embodied in apropagated signal, for execution by, or to control the operation of,data processing apparatus (e.g., a programmable processor, a computer,or multiple computers). A computer program (also known as a program,software, software application, or code) can be written in any form ofprogramming language, including compiled or interpreted languages, andit can be deployed in any form, including as a stand-alone program or asa module, component, subroutine, or other unit suitable for use in acomputing environment. A computer program does not necessarilycorrespond to a file. A program can be stored in a portion of a filethat holds other programs or data, in a single file dedicated to theprogram in question, or in multiple coordinated files (e.g., files thatstore one or more modules, sub-programs, or portions of code). Acomputer program can be deployed to be executed on one computer or onmultiple computers at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification, includingthe method steps of the subject matter described herein, can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions of the subject matter describedherein by operating on input data and generating output. The processesand logic flows can also be performed by, and apparatus of the subjectmatter described herein can be implemented as, special purpose logiccircuitry, e.g., a GPU (graphical processing unit), an FPGA (fieldprogrammable gate array) or an ASIC (application-specific integratedcircuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processor of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The essential elements of a computer area processor for executing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto-optical disks, or optical disks. Information carrierssuitable for embodying computer program instructions and data includeall forms of non-volatile memory, including by way of examplesemiconductor memory devices, (e.g., EPROM, EEPROM, and flash memorydevices); magnetic disks, (e.g., internal hard disks or removabledisks); magneto-optical disks; and optical disks (e.g., CD and DVDdisks). The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

To provide for interaction with a user, the subject matter describedherein can be implemented on a computer having a display device, e.g., aCRT (cathode ray tube) or LCD (liquid crystal display) monitor, fordisplaying information to the user and a keyboard and a pointing device,(e.g., a mouse or a trackball), by which the user can provide input tothe computer. Other kinds of devices can be used to provide forinteraction with a user as well. For example, feedback provided to theuser can be any form of sensory feedback, (e.g., visual feedback,auditory feedback, or tactile feedback), and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The techniques described herein can be implemented using one or moremodules. As used herein, the term “module” refers to computing software,firmware, hardware, and/or various combinations thereof. At a minimum,however, modules are not to be interpreted as software that is notimplemented on hardware, firmware, or recorded on a non-transitoryprocessor readable recordable storage medium (i.e., modules are notsoftware per se). Indeed “module” is to be interpreted to always includeat least some physical, non-transitory hardware such as a part of aprocessor or computer. Two different modules can share the same physicalhardware (e.g., two different modules can use the same processor andnetwork interface). The modules described herein can be combined,integrated, separated, and/or duplicated to support variousapplications. Also, a function described herein as being performed at aparticular module can be performed at one or more other modules and/orby one or more other devices instead of or in addition to the functionperformed at the particular module. Further, the modules can beimplemented across multiple devices and/or other components local orremote to one another. Additionally, the modules can be moved from onedevice and added to another device, and/or can be included in bothdevices.

The subject matter described herein can be implemented in a computingsystem that includes a back-end component (e.g., a data server), amiddleware component (e.g., an application server), or a front-endcomponent (e.g., a client computer having a graphical user interface ora web browser through which a user can interact with an implementationof the subject matter described herein), or any combination of suchback-end, middleware, and front-end components. The components of thesystem can be interconnected by any form or medium of digital datacommunication, e.g., a communication network. Examples of communicationnetworks include a local area network (“LAN”) and a wide area network(“WAN”), e.g., the Internet.

Approximating language, as used herein throughout the specification andclaims, may be applied to modify any quantitative representation thatcould permissibly vary without resulting in a change in the basicfunction to which it is related. Accordingly, a value modified by a termor terms, such as “about,” “approximately,” and “substantially,” are notto be limited to the precise value specified. In at least someinstances, the approximating language may correspond to the precision ofan instrument for measuring the value. Here and throughout thespecification and claims, range limitations may be combined and/orinterchanged, such ranges are identified and include all the sub-rangescontained therein unless context or language indicates otherwise.

One skilled in the art will appreciate further features and advantagesof the invention based on the above-described embodiments. Accordingly,the present application is not to be limited by what has beenparticularly shown and described, except as indicated by the appendedclaims. All publications and references cited herein are expresslyincorporated by reference in their entirety.

The invention claimed is:
 1. A method comprising: determining, by acomputing system including at least one data processor, a configurationof a plurality of Near-Field sensors and a plurality of Far-Fieldsensors to be positioned within a gas production and distributionenvironment; positioning the plurality of Near-Field sensors and theplurality of Far-Field sensors in the gas production and distributionenvironment in the determined configuration, the plurality of Near-Fieldsensors and the plurality of Far-Field sensors including one or morewind sensors and one or more gas sensors communicatively coupled to theat least one data processor; receiving, by the at least one dataprocessor, Near-Field sensor data from the plurality of Near-Fieldsensors and Far-Field sensor data from the plurality of Far-Fieldsensors, the Near-Field sensor data and Far-Field sensor data associatedwith a gas emission from an emission source within the gas productionand distribution environment; determining, by the at least one dataprocessor, gas concentration data associated with the gas emission basedon the Near-Field and Far-Field sensor data; determining, by the atleast one data processor, an emission rate corresponding to the gasemission; generating, by the at least one data processor, emission datacorresponding to the gas emission, the emission data including thedetermined emission rate and one or more source locations associatedwith the gas emission; and providing the emission data.
 2. The method ofclaim 1, wherein determining the emission rate includes providing thegas concentration data as an input to a Near-Field dispersion model. 3.The method of claim 2, wherein determining the emission rate furtherincludes receiving Far-Field wind data as an input to the Near-Fielddispersion model.
 4. The method of claim 2, wherein determining theemission rate further includes determining sensor location shape factorsand providing the sensor location shape factors as an input to theNear-Field dispersion model.
 5. The method of claim 1, wherein the oneor more sensors are positioned in a grid with respect to the gasproduction and distribution environment.
 6. The method of claim 1,wherein the emission rate is determined based on wind data generated bythe one or more wind sensors of the plurality of Near-Field sensorsand/or by the one or more wind sensors of the plurality of Far-Fieldsensors.
 7. The method of claim 6, wherein the Far-Field sensor ispositioned at a height of 4 to 8 feet above the ground.
 8. The method ofclaim 6, wherein the Far-Field sensor is positioned between 10 to 100feet, between 30 to 100 feet, or 50 to 200 feet from a potentialemission source.
 9. The method of claim 6, wherein the Near-Field sensoris configured on a mobile platform.
 10. The method of claim 9, whereinthe mobile platform includes a manned ground vehicle, an unmanned groundvehicle, a manned aerial vehicle, an unmanned aerial vehicle, a mannedsurface vehicle, an unmanned surface vehicle, a robot, or a mobileplatform attached to a human in motion.
 11. A system comprising: aplurality of Near-Field sensors including one or more wind sensors andone or more gas sensors positioned in a gas production and distributionenvironment; a plurality of Far-Field sensors including one or more windsensors and one or more gas sensors positioned in the gas production anddistribution environment; a first computing device communicativelycoupled to the plurality of Near-Field sensors and the plurality ofFar-Field sensors, and including a data processor and a memory storingcomputer-readable instructions, the processor configured to execute thecomputer-readable instructions, which when executed, cause the processorto perform operations including receiving Near-Field sensor data andFar-Field sensor data from the plurality of Near-Field sensors and theplurality of Far-Field sensors, the sensor data associated with a gasbeing emitted from an emission source, determining gas concentrationdata associated with the gas emission, determining an emission ratecorresponding to the gas emission, generating emission datacorresponding to the gas emission, the emission data including thedetermined emission rate and one or more source locations associatedwith the gas emission, and providing the emission data; and a secondcomputing device coupled to the first computing device via a network,the second computing device including a display configured to presentthe emission data via the display.
 12. The system of claim 11, whereinthe processor is further configured to determine the emission rate basedon providing the gas concentration data as an input to a Near-Fielddispersion model.
 13. The system of claim 12, wherein the processor isfurther configured to determine the emission rate based on providingFar-Field wind data as an input to the Near-Field dispersion model. 14.The system of claim 12, wherein the processor is further configured todetermine the emission rate based on determining sensor location shapefactors and providing the sensor location shape factors as an input tothe Near-Field dispersion model.
 15. The system of claim 11, wherein theone or more sensors are positioned in a grid with respect to the gasproduction and distribution environment.
 16. The system of claim 11,wherein the emission rate is determined based on wind data generated bythe one or more wind sensors of the plurality of Near-Field sensorsand/or by the one or more wind sensors of the plurality of Far-Fieldsensors.
 17. The system of claim 15, wherein the Far-Field sensor ispositioned between 10 to 100 feet, between 30 to 100 feet, or 50 to 200feet from a potential emission source.
 18. The system of claim 15,wherein the Near-Field sensor is configured on a mobile platform. 19.The system of claim 18, wherein the mobile platform includes a mannedground vehicle, an unmanned ground vehicle, a manned aerial vehicle, anunmanned aerial vehicle, a manned surface vehicle, an unmanned surfacevehicle, a robot, or a mobile platform attached to a human in motion.20. A non-transitory computer readable storage medium containing programinstructions, which when executed by at least one data processor causesthe at least one data processor to perform operations comprising:determining, by a first computing device, a configuration of a pluralityof Near-Field sensors and a plurality of Far-Field sensors to bepositioned within a gas production and distribution environment, theplurality of Near-Field sensors and the plurality of Far-Field sensorsincluding one or more wind sensors and one or more gas sensorscommunicatively coupled to the first computing device; receiving, by thefirst computing device, Near-Field sensor data from the plurality ofNear-Field sensors and Far-Field sensor data from the plurality ofFar-Field sensors, the Near-Field sensor data and the Far-Field sensordata associated with a gas being emitted from an emission source;determining, by the first computing device, gas concentration dataassociated with the gas emission; generating, by the first computingdevice, emission data, the emission data including the determinedemission rate corresponding to the gas emission and one or more sourcelocations associated with the gas emission; transmitting, by the firstcomputing device to a second computing device via a network, theemission data; and providing, via a display of the second computingdevice, the emission data for display.